API Rate Limiter System Design

Introduction

API rate limiting is essential to prevent abuse, ensure fair usage, and protect backend resources. A robust rate limiter must work across distributed systems and scale with user demand.

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Problem Statement

How can we design a rate limiter that enforces per-user or per-IP limits, works across multiple servers, and is resilient to failures?

System Requirements

• Enforce configurable limits (e.g., 100 requests/minute/user).
• Low latency and high throughput.
• Distributed and fault-tolerant.
• Support for burst and steady rate limits.
• Real-time monitoring and alerting.

High-Level Design

The system consists of:

• API Gateway: Intercepts requests and checks rate limits.
• Rate Limiter Service: Tracks request counts and enforces limits.
• Data Store: Stores counters and timestamps (e.g., Redis).
• Monitoring: Tracks usage and triggers alerts.

Key Components

• Token Bucket/Leaky Bucket Algorithms: Allow bursts while enforcing average rate.
• Sliding Window Counters: More accurate rate limiting over time windows.
• Distributed Store: Use Redis or Memcached for shared counters.
• Failover: Graceful degradation if the rate limiter is unavailable.

Challenges

• Consistency: Ensuring accurate limits across distributed nodes.
• Performance: Minimizing latency for each API call.
• Scalability: Handling millions of users and requests per second.
• Eviction: Cleaning up old counters to save memory.

Example Technologies

• Redis: Fast, atomic operations for counters.
• API Gateway: NGINX, Envoy, AWS API Gateway.
• Monitoring: Prometheus, Grafana.

Conclusion

A distributed API rate limiter is critical for reliable, fair, and secure APIs. By leveraging efficient algorithms and scalable data stores, you can enforce limits without sacrificing performance or user experience.